





import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow


@require_torch
class     A     (unittest.TestCase       ):
			'''simple docstring'''




			def   a_			(     self  :       Any  )      ->      Any:







					"""simple docstring"""






					A__     =  """ylacombe/bark-small"""
					A__     =  tempfile.mkdtemp()
					A__     =  """en_speaker_1"""
					A__     =  """This is a test string"""
					A__     =  """speaker_embeddings_path.json"""
					A__     =  """speaker_embeddings"""






			def   a_			(     self  :       Optional[Any]				,      **__lowerCAmelCase  :       str  )      ->      Optional[int]:







					"""simple docstring"""






					return AutoTokenizer.from_pretrained(self.checkpoint				,      **_lowerCamelCase  )






			def   a_			(     self  :       Dict  )      ->      Union[str, Any]:







					"""simple docstring"""






					shutil.rmtree(self.tmpdirname  )






			def   a_			(     self  :       Any  )      ->      Any:







					"""simple docstring"""






					A__     =  self.get_tokenizer()

					A__     =  BarkProcessor(tokenizer=_lowerCamelCase  )

					processor.save_pretrained(self.tmpdirname  )
					A__     =  BarkProcessor.from_pretrained(self.tmpdirname  )

					self.assertEqual(processor.tokenizer.get_vocab()				,      tokenizer.get_vocab()  )






			@slow
			def   a_			(     self  :       Tuple  )      ->      Dict:







					"""simple docstring"""






					A__     =  BarkProcessor.from_pretrained(
					    pretrained_processor_name_or_path=self.checkpoint				,      speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,      )
					processor.save_pretrained(
					    self.tmpdirname				,      speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,      speaker_embeddings_directory=self.speaker_embeddings_directory				,      )

					A__     =  self.get_tokenizer(bos_token="""(BOS)"""				,      eos_token="""(EOS)"""  )

					A__     =  BarkProcessor.from_pretrained(
					    self.tmpdirname				,      self.speaker_embeddings_dict_path				,      bos_token="""(BOS)"""				,      eos_token="""(EOS)"""				,      )

					self.assertEqual(processor.tokenizer.get_vocab()				,      tokenizer_add_kwargs.get_vocab()  )






			def   a_			(     self  :       int  )      ->      Any:







					"""simple docstring"""






					A__     =  BarkProcessor.from_pretrained(
					    pretrained_processor_name_or_path=self.checkpoint				,      speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,      )

					A__     =  35
					A__     =  2
					A__     =  8

					A__     =  {
					    """semantic_prompt""": np.ones(_lowerCamelCase  ),
					    """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)  ),
					    """fine_prompt""": np.ones((nb_codebooks_total, seq_len)  ),
					}

					# test providing already loaded voice_preset
					A__     =  processor(text=self.input_string				,      voice_preset=_lowerCamelCase  )

					A__     =  inputs["""history_prompt"""]
					for key in voice_preset:
							self.assertListEqual(voice_preset[key].tolist()				,      processed_voice_preset.get(_lowerCamelCase				,      np.array([]  )  ).tolist()  )

					# test loading voice preset from npz file
					A__     =  os.path.join(self.tmpdirname				,      """file.npz"""  )
					np.savez(_lowerCamelCase				,      **_lowerCamelCase  )
					A__     =  processor(text=self.input_string				,      voice_preset=_lowerCamelCase  )
					A__     =  inputs["""history_prompt"""]

					for key in voice_preset:
							self.assertListEqual(voice_preset[key].tolist()				,      processed_voice_preset.get(_lowerCamelCase				,      np.array([]  )  ).tolist()  )

					# test loading voice preset from the hub
					A__     =  processor(text=self.input_string				,      voice_preset=self.voice_preset  )







			def   a_			(     self  :       Dict  )      ->      List[Any]:







					"""simple docstring"""






					A__     =  self.get_tokenizer()

					A__     =  BarkProcessor(tokenizer=_lowerCamelCase  )

					A__     =  processor(text=self.input_string  )

					A__     =  tokenizer(
					    self.input_string				,      padding="""max_length"""				,      max_length=2_56				,      add_special_tokens=_lowerCamelCase				,      return_attention_mask=_lowerCamelCase				,      return_token_type_ids=_lowerCamelCase				,      )

					for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key]				,      encoded_processor[key].squeeze().tolist()  )




"""simple docstring"""


def      lowerCamelCase_					(   __lowerCAmelCase							)				->   list:




						'''simple docstring'''



						if len(__lowerCAmelCase							) <= 1:
												return [tuple(__lowerCAmelCase							)]

						lowerCamelCase__   =[]

						def generate(__lowerCAmelCase  ,       __lowerCAmelCase							):
												lowerCamelCase__   =[0] * n
												res.append(tuple(__lowerCAmelCase							)							)

												lowerCamelCase__   =0
												while i < n:
																		if c[i] < i:
																								if i % 2 == 0:
																														lowerCamelCase__   , lowerCamelCase__   =arr[i], arr[0]
																								else:
																														lowerCamelCase__   , lowerCamelCase__   =arr[i], arr[c[i]]
																								res.append(tuple(__lowerCAmelCase							)							)
																								c[i] += 1
																								lowerCamelCase__   =0
																		else:
																								lowerCamelCase__   =0
																								i += 1

						generate(len(__lowerCAmelCase							)  ,       __lowerCAmelCase							)
						return res


if __name__ == "__main__":
	a				=input('Enter numbers separated by a comma:\n').strip()
	a				=[int(item) for item in user_input.split(',')]
	print(heaps(arr))


